OpenRaiser/Intern-Atlas huggingface dataset:
GR-GAN: Gradual Refinement Text-to-image Generation
Venue: arXiv cs.CV 2022
Year: 2022

A good Text-to-Image model should not only generate high quality images, but also ensure the consistency between the text and the generated image. Previous models failed to simultaneously fix both sides well. This paper proposes a Gradual Refinement Generative Adversarial Network (GR-GAN) to alleviates the problem efficiently. A GRG module is designed to generate images from low resolution to high resolution with the corresponding text constraints from coarse granularity (sentence) to fine granularity (word) stage by stage, a ITM module is designed to provide image-text matching losses at both sentence-image level and word-region level for corresponding stages. We also introduce a new metric Cross-Model Distance (CMD) for simultaneously evaluating image quality and image-text consistency. Experimental results show GR-GAN significant outperform previous models, and achieve new state-of-the-art on both FID and CMD. A detailed analysis demonstrates the efficiency of different generation stages in GR-GAN.
Cooperative Reinforcement Learning on Traffic Signal Control
Venue: arXiv cs.AI 2022
Year: 2022

Traffic signal control is a challenging real-world problem aiming to minimize overall travel time by coordinating vehicle movements at road intersections. Existing traffic signal control systems in use still rely heavily on oversimplified information and rule-based methods. Specifically, the periodicity of green/red light alternations can be considered as a prior for better planning of each agent in policy optimization. To better learn such adaptive and predictive priors, traditional RL-based methods can only return a fixed length from predefined action pool with only local agents. If there is no cooperation between these agents, some agents often make conflicts to other agents and thus decrease the whole throughput. This paper proposes a cooperative, multi-objective architecture with age-decaying weights to better estimate multiple reward terms for traffic signal control optimization, which termed COoperative Multi-Objective Multi-Agent Deep Deterministic Policy Gradient (COMMA-DDPG). Two types of agents running to maximize rewards of different goals - one for local traffic optimization at each intersection and the other for global traffic waiting time optimization. The global agent is used to guide the local agents as a means for aiding faster learning but not used in the inference phase. We also provide an analysis of solution existence together with convergence proof for the proposed RL optimization. Evaluation is performed using real-world traffic data collected using traffic cameras from an Asian country. Our method can effectively reduce the total delayed time by 60\%. Results demonstrate its superiority when compared to SoTA methods.
Undecidability of Network Coding, Conditional Information Inequalities, and Conditional Independence Implication
Venue: arXiv cs.IT 2022
Year: 2022

We resolve three long-standing open problems, namely the (algorithmic) decidability of network coding, the decidability of conditional information inequalities, and the decidability of conditional independence implication among random variables, by showing that these problems are undecidable. The proof utilizes a construction inspired by Herrmann's arguments on embedded multivalued database dependencies, a network studied by Dougherty, Freiling and Zeger, together with a novel construction to represent group automorphisms on top of the network.
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
Venue: arXiv cs.CV 2022
Year: 2022

Due to the intractability of characterizing everything that looks unlike the normal data, anomaly detection (AD) is traditionally treated as an unsupervised problem utilizing only normal samples. However, it has recently been found that unsupervised image AD can be drastically improved through the utilization of huge corpora of random images to represent anomalousness; a technique which is known as Outlier Exposure. In this paper we show that specialized AD learning methods seem unnecessary for state-of-the-art performance, and furthermore one can achieve strong performance with just a small collection of Outlier Exposure data, contradicting common assumptions in the field of AD. We find that standard classifiers and semi-supervised one-class methods trained to discern between normal samples and relatively few random natural images are able to outperform the current state of the art on an established AD benchmark with ImageNet. Further experiments reveal that even one well-chosen outlier sample is sufficient to achieve decent performance on this benchmark (79.3% AUC). We investigate this phenomenon and find that one-class methods are more robust to the choice of training outliers, indicating that there are scenarios where these are still more useful than standard classifiers. Additionally, we include experiments that delineate the scenarios where our results hold. Lastly, no training samples are necessary when one uses the representations learned by CLIP, a recent foundation model, which achieves state-of-the-art AD results on CIFAR-10 and ImageNet in a zero-shot setting.
On the Paradox of Learning to Reason from Data
Venue: arXiv cs.CL 2022
Year: 2022

Logical reasoning is needed in a wide range of NLP tasks. Can a BERT model be trained end-to-end to solve logical reasoning problems presented in natural language? We attempt to answer this question in a confined problem space where there exists a set of parameters that perfectly simulates logical reasoning. We make observations that seem to contradict each other: BERT attains near-perfect accuracy on in-distribution test examples while failing to generalize to other data distributions over the exact same problem space. Our study provides an explanation for this paradox: instead of learning to emulate the correct reasoning function, BERT has in fact learned statistical features that inherently exist in logical reasoning problems. We also show that it is infeasible to jointly remove statistical features from data, illustrating the difficulty of learning to reason in general. Our result naturally extends to other neural models and unveils the fundamental difference between learning to reason and learning to achieve high performance on NLP benchmarks using statistical features.
Accelerating the creation of instance segmentation training sets through bounding box annotation
Venue: arXiv cs.CV 2022
Year: 2022

Collecting image annotations remains a significant burden when deploying CNN in a specific applicative context. This is especially the case when the annotation consists in binary masks covering object instances. Our work proposes to delineate instances in three steps, based on a semi-automatic approach: (1) the extreme points of an object (left-most, right-most, top, bottom pixels) are manually defined, thereby providing the object bounding-box, (2) a universal automatic segmentation tool like Deep Extreme Cut is used to turn the bounded object into a segmentation mask that matches the extreme points; and (3) the predicted mask is manually corrected. Various strategies are then investigated to balance the human manual annotation resources between bounding-box definition and mask correction, including when the correction of instance masks is prioritized based on their overlap with other instance bounding-boxes, or the outcome of an instance segmentation model trained on a partially annotated dataset. Our experimental study considers a teamsport player segmentation task, and measures how the accuracy of the Panoptic-Deeplab instance segmentation model depends on the human annotation resources allocation strategy. It reveals that the sole definition of extreme points results in a model accuracy that would require up to 10 times more resources if the masks were defined through fully manual delineation of instances. When targeting higher accuracies, prioritizing the mask correction among the training set instances is also shown to save up to 80\% of correction annotation resources compared to a systematic frame by frame correction of instances, for a same trained instance segmentation model accuracy.
Seeded Hierarchical Clustering for Expert-Crafted Taxonomies
Venue: arXiv cs.CL 2022
Year: 2022

Practitioners from many disciplines (e.g., political science) use expert-crafted taxonomies to make sense of large, unlabeled corpora. In this work, we study Seeded Hierarchical Clustering (SHC): the task of automatically fitting unlabeled data to such taxonomies using only a small set of labeled examples. We propose HierSeed, a novel weakly supervised algorithm for this task that uses only a small set of labeled seed examples. It is both data and computationally efficient. HierSeed assigns documents to topics by weighing document density against topic hierarchical structure. It outperforms both unsupervised and supervised baselines for the SHC task on three real-world datasets.
Evolution of biomedical innovation quantified via billions of distinct article-level MeSH keyword combinations
Venue: arXiv cs.DL 2022
Year: 2022

We develop a systematic approach to measuring combinatorial innovation in the biomedical sciences based upon the comprehensive ontology of Medical Subject Headings (MeSH). This approach leverages an expert-defined knowledge ontology that features both breadth (27,875 MeSH analyzed across 25 million articles indexed by PubMed from 1902 onwards) and depth (we differentiate between Major and Minor MeSH terms to identify differences in the knowledge network representation constructed from primary research topics only). With this level of uniform resolution we differentiate between three different modes of innovation contributing to the combinatorial knowledge network: (i) conceptual innovation associated with the emergence of new concepts and entities (measured as the entry of new MeSH); and (ii) recombinant innovation, associated with the emergence of new combinations, which itself consists of two types: peripheral (i.e., combinations involving new knowledge) and core (combinations comprised of pre-existing knowledge only). Another relevant question we seek to address is whether examining triplet and quartet combinations, in addition to the more traditional dyadic or pairwise combinations, provide evidence of any new phenomena associated with higher-order combinations. Analysis of the size, growth, and coverage of combinatorial innovation yield results that are largely independent of the combination order, thereby suggesting that the common dyadic approach is sufficient to capture essential phenomena. Our main results are twofold: (a) despite the persistent addition of new MeSH terms, the network is densifying over time meaning that scholars are increasingly exploring and realizing the vast space of all knowledge combinations; and (b) conceptual innovation is increasingly concentrated within single research articles, a harbinger of the recent paradigm shift towards convergence science.
Forecasting of Non-Stationary Sales Time Series Using Deep Learning
Venue: arXiv cs.LG 2022
Year: 2022

The paper describes the deep learning approach for forecasting non-stationary time series with using time trend correction in a neural network model. Along with the layers for predicting sales values, the neural network model includes a subnetwork block for the prediction weight for a time trend term which is added to a predicted sales value. The time trend term is considered as a product of the predicted weight value and normalized time value. The results show that the forecasting accuracy can be essentially improved for non-stationary sales with time trends using the trend correction block in the deep learning model.
Attitudes, willingness, and resources to cover Article Publishing Charges (APC): the influence of age, position, income level country, discipline and open access habits
Venue: arXiv cs.DL 2022
Year: 2022

The rise of open access (OA) publishing has been followed by the expansion of the Article Publishing Charges (APC) that moves the financial burden of scholarly journal publishing from readers to authors. We introduce the results of an international randomly selected sampled survey (N=3,422) that explores attitudes towards this pay-to-publish or Gold OA model among scholars. We test the predictor role of age, professional position, discipline, and income-level country in this regard. We found that APCs are perceived more as a global threat to Science than a deterrent to personal professional careers. Academics in low and lower-middle income level countries hold the most unfavorable opinions about the APC system. The less experimental disciplines held more negative perceptions of APC compared to STEM and the Life Sciences. Age and access to external funding stood as negative predictors of refusal to pay to publish. Commitment to OA self-archiving predicted the negative global perception of the APC. We conclude that access to external research funds influences the acceptance and the particular perception of the pay to publish model, remarking the economic dimension of the problem and warning about issues in the inequality between center and periphery.
PCA-Boosted Autoencoders for Nonlinear Dimensionality Reduction in Low Data Regimes
Venue: arXiv cs.LG 2022
Year: 2022

Autoencoders (AE) provide a useful method for nonlinear dimensionality reduction but are ill-suited for low data regimes. Conversely, Principal Component Analysis (PCA) is data-efficient but is limited to linear dimensionality reduction, posing a problem when data exhibits inherent nonlinearity. This presents a challenge in various scientific and engineering domains such as the nanophotonic component design, where data exhibits nonlinear features while being expensive to obtain due to costly real measurements or resource-consuming solutions of partial differential equations. To address this difficulty, we propose a technique that harnesses the best of both worlds: an autoencoder that leverages PCA to perform well on scarce nonlinear data. Specifically, we outline a numerically robust PCA-based initialization of AE, which, together with the parameterized ReLU activation function, allows the training process to start from an exact PCA solution and improve upon it. A synthetic example is presented first to study the effects of data nonlinearity and size on the performance of the proposed method. We then evaluate our method on several nanophotonic component design problems where obtaining useful data is expensive. To demonstrate universality, we also apply it to tasks in other scientific domains: a benchmark breast cancer dataset and a gene expression dataset. We show that our proposed approach is substantially better than both PCA and randomly initialized AE in the majority of low-data regime cases we consider, or at least is comparable to the best of either of the other two methods.
A Proof-Generating C Code Generator for ACL2 Based on a Shallow Embedding of C in ACL2
Venue: arXiv cs.LO 2022
Year: 2022

This paper describes a C code generator for ACL2 that recognizes ACL2 representations of C constructs, according to a shallow embedding of C in ACL2, and translates those representations to the represented C constructs. The code generator also generates ACL2 theorems asserting the correctness of the C code with respect to the ACL2 code. The code generator currently supports a limited but growing subset of C that already suffices for some interesting programs. This paper also offers a general perspective on language embedding and code generation.
Hardware/Software Co-Assurance using the Rust Programming Language and ACL2
Venue: arXiv cs.LO 2022
Year: 2022

The Rust programming language has garnered significant interest and use as a modern, type-safe, memory-safe, and potentially formally analyzable programming language. Our interest in Rust stems from its potential as a hardware/software co-assurance language, with application to critical systems such as autonomous vehicles. We report on the first known use of Rust as a High-Level Synthesis (HLS) language. Most incumbent HLS languages are a subset of C. A Rust-based HLS brings a single modern, type-safe, and memory-safe expression language for both hardware and software realizations with high assurance. As a a study of the suitability of Rust as an HLS, we have crafted a Rust subset, inspired by Russinoff's Restricted Algorithmic C (RAC), which we have imaginatively named Restricted Algorithmic Rust, or RAR. In our first implementation of a RAR toolchain, we simply transpile the RAR source into RAC. By so doing, we leverage a number of existing hardware/software co-assurance tools with a minimum investment of time and effort. In this paper, we describe the RAR Rust subset, detail our prototype RAR toolchain, and describe the implementation and verification of several representative algorithms and data structures written in RAR, with proofs of correctness conducted using the ACL2 theorem prover.
D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat
Venue: arXiv cs.CL 2022
Year: 2022

In a depression-diagnosis-directed clinical session, doctors initiate a conversation with ample emotional support that guides the patients to expose their symptoms based on clinical diagnosis criteria. Such a dialogue system is distinguished from existing single-purpose human-machine dialog systems, as it combines task-oriented and chit-chats with uniqueness in dialogue topics and procedures. However, due to the social stigma associated with mental illness, the dialogue data related to depression consultation and diagnosis are rarely disclosed. Based on clinical depression diagnostic criteria ICD-11 and DSM-5, we designed a 3-phase procedure to construct D$^4$: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat, which simulates the dialogue between doctors and patients during the diagnosis of depression, including diagnosis results and symptom summary given by professional psychiatrists for each conversation. Upon the newly-constructed dataset, four tasks mirroring the depression diagnosis process are established: response generation, topic prediction, dialog summary, and severity classification of depressive episode and suicide risk. Multi-scale evaluation results demonstrate that a more empathy-driven and diagnostic-accurate consultation dialogue system trained on our dataset can be achieved compared to rule-based bots.
LOCUS 2.0: Robust and Computationally Efficient Lidar Odometry for Real-Time Underground 3D Mapping
Venue: arXiv cs.RO 2022
Year: 2022

Lidar odometry has attracted considerable attention as a robust localization method for autonomous robots operating in complex GNSS-denied environments. However, achieving reliable and efficient performance on heterogeneous platforms in large-scale environments remains an open challenge due to the limitations of onboard computation and memory resources needed for autonomous operation. In this work, we present LOCUS 2.0, a robust and computationally-efficient \lidar odometry system for real-time underground 3D mapping. LOCUS 2.0 includes a novel normals-based \morrell{Generalized Iterative Closest Point (GICP)} formulation that reduces the computation time of point cloud alignment, an adaptive voxel grid filter that maintains the desired computation load regardless of the environment's geometry, and a sliding-window map approach that bounds the memory consumption. The proposed approach is shown to be suitable to be deployed on heterogeneous robotic platforms involved in large-scale explorations under severe computation and memory constraints. We demonstrate LOCUS 2.0, a key element of the CoSTAR team's entry in the DARPA Subterranean Challenge, across various underground scenarios. We release LOCUS 2.0 as an open-source library and also release a \lidar-based odometry dataset in challenging and large-scale underground environments. The dataset features legged and wheeled platforms in multiple environments including fog, dust, darkness, and geometrically degenerate surroundings with a total of $11~h$ of operations and $16~km$ of distance traveled.
Influence of perspective taking through robotic virtual agents on prosocial behavior
Venue: arXiv cs.HC 2022
Year: 2022

Perspective taking, which allows people to imagine another's thinking and goals, is known to be an effective method for promoting prosocial behaviors in human-computer interactions. However, most of the previous studies have focused on simulating human-human interactions in the real world by offering participants experiences related to various moral tasks through the use of human-like virtual agents. In this study, we investigated whether taking the perspective of a different robot in a robot-altruistic task would influence the social behaviors of participants in a dictator game. Our findings showed that participants who watched the help-receiver view exhibited more altruistic behaviors toward a robot than those who watched the help-provider view. We also found that, after watching robots from two different viewpoints in the task, participants did not change their behavior toward another participant.
Package Theft Detection from Smart Home Security Cameras
Venue: arXiv cs.CV 2022
Year: 2022

Package theft detection has been a challenging task mainly due to lack of training data and a wide variety of package theft cases in reality. In this paper, we propose a new Global and Local Fusion Package Theft Detection Embedding (GLF-PTDE) framework to generate package theft scores for each segment within a video to fulfill the real-world requirements on package theft detection. Moreover, we construct a novel Package Theft Detection dataset to facilitate the research on this task. Our method achieves 80% AUC performance on the newly proposed dataset, showing the effectiveness of the proposed GLF-PTDE framework and its robustness in different real scenes for package theft detection.
Increasing Cellular Network Energy Efficiency for Railway Corridors
Venue: arXiv cs.NI 2022
Year: 2022

Modern trains act as Faraday cages making it challenging to provide high cellular data capacities to passengers. A solution is the deployment of linear cells along railway tracks, forming a cellular corridor. To provide a sufficiently high data capacity, many cell sites need to be installed at regular distances. However, such cellular corridors with high power sites in short distance intervals are not sustainable due to the infrastructure power consumption. To render railway connectivity more sustainable, we propose to deploy fewer high-power radio units with intermediate low-power support repeater nodes. We show that these repeaters consume only 5 % of the energy of a regular cell site and help to maintain the same data capacity in the trains. In a further step, we introduce a sleep mode for the repeater nodes that enables autonomous solar powering and even eases installation because no cables to the relays are needed.
Charon: a FrameNet Annotation Tool for Multimodal Corpora
Venue: arXiv cs.CL 2022
Year: 2022

This paper presents Charon, a web tool for annotating multimodal corpora with FrameNet categories. Annotation can be made for corpora containing both static images and video sequences paired - or not - with text sequences. The pipeline features, besides the annotation interface, corpus import and pre-processing tools.